Using AI in Product Development with CENIT
AI-driven engineering for industry:
Develop faster, manufacture smarter
AI-driven engineering: Automate product development, manage options efficiently
From concept design to manufacturing, AI is transforming product development. Whether creating digital twins, performing simulation-based topology optimization, or developing intelligent assistance systems, AI enables a data-driven engineering process that adapts quickly and flexibly to new requirements. This allows you to optimize your design processes by bringing fast innovative solutions to market.
The AI-supported solutions from CENIT and its technology partners help you shorten development times, use resources in a targeted manner, and ensure from the beginning the quality of your products.
Digital twins and AI in manufacturing: 3D UNIV+RSES
AI technologies deliver the greatest added value when they are deeply integrated into existing systems and processes. This allows them to access company- and product-specific data contexts and generate results that are precisely tailored to the specific use case.
Dassault Systèmes' 3D UNIV+RSES architecture makes that possible, by embedding generative AI directly into the intellectual property lifecycle management of the 3DEXPERIENCE platform. This enables manufacturing companies to create, simulate, and AI-optimize digital twins of products, processes and entire systems.
- Simulation and comparison of complex production scenarios
- Training of intelligent AI engines for specific manufacturing applications
- Validation and scaling of your expertise in digital environments
Through this architecture manufacturing processes are digitally displayed from production planning to virtual commissioning. Companies thus benefit from secure industrial environments in which they can apply AI applications directly to their real-world manufacturing scenarios and continuously develop them further, while fully protecting their intellectual property.
Topology optimization through AI and simulation-based modeling
Generative AI applications enable manufacturers to create topology-optimized components and assemblies for innovative, high-performance structures. Thousands of design alternatives can be analyzed and evaluated as early as the concept phase. Concept studies for different materials can also be carried out efficiently, for example to optimize layout and topology in terms of strength, stiffness, or weight efficiency.
This is made possible by solutions for model-based systems engineering (MBSE), which can be used to design, analyze, and evaluate even complex systems in simulation-capable modeling environments. Solutions such as SIMULIA Tosca enable simulation-based topology, shape, and sizing optimizations directly in the design process. Based on finite element analyses, component geometries with optimal material distribution are automatically generated, tailored to real boundary conditions, manufacturing processes, and mechanical target values.
As an integral part of a data-driven development process, AI-powered solutions like these support the creation of efficient, load-optimized designs. In doing so, they form a key pillar of digital, future-ready engineering workflows.
Engineering Copilot: Intelligent Assistance for Development and Manufacturing
Even in the form of intelligent assistants, AI unfolds great potential to make development and manufacturing processes more efficient. An example of this is AURA, the AI assistant of the 3DEXPERIENCE platform from Dassault Systèmes. With the help of the Generative 3D Component function, precise digital twins can be generated from photos of existing components. These are suitable as a basis for virtual tests that realistically simulate real workloads and conditions.
Furthermore, AURA automatically recognizes regulatory requirements and proactively points out the regulations that need to be adhered to. This ensures that companies guarantee each component is not only functional but also compliant with regulations. The AI co-pilot thus ensures a more efficient, targeted production – and secures product quality already in the early design phase.
End-to-End Integration as the Basis for AI-driven Engineering
The premise for AI-supported optimization of your engineering processes is a continuous digital data foundation. Consistent data management throughout the entire product lifecycle creates the basis for realistic simulating systems, intelligently automating processes, and purposefully advancing products.
CENIT accompanies you on your journey to end-to-end digitalization – with comprehensive process consulting, scalable system solutions, and in-depth expertise in the field of modeling and simulation.
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Buchholz Contact me now
Vice President Sales
CENIT AG